{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nColormap Normalization\n======================\n\nObjects that use colormaps by default linearly map the colors in the\ncolormap from data values *vmin* to *vmax*. For example::\n\n pcm = ax.pcolormesh(x, y, Z, vmin=-1., vmax=1., cmap='RdBu_r')\n\nwill map the data in *Z* linearly from -1 to +1, so *Z=0* will\ngive a color at the center of the colormap *RdBu_r* (white in this\ncase).\n\nMatplotlib does this mapping in two steps, with a normalization from\nthe input data to [0, 1] occurring first, and then mapping onto the\nindices in the colormap. Normalizations are classes defined in the\n:func:matplotlib.colors module. The default, linear normalization\nis :func:matplotlib.colors.Normalize.\n\nArtists that map data to color pass the arguments *vmin* and *vmax* to\nconstruct a :func:matplotlib.colors.Normalize instance, then call it:\n\n.. ipython::\n\n In : import matplotlib as mpl\n\n In : norm = mpl.colors.Normalize(vmin=-1.,vmax=1.)\n\n In : norm(0.)\n Out: 0.5\n\nHowever, there are sometimes cases where it is useful to map data to\ncolormaps in a non-linear fashion.\n\nLogarithmic\n-----------\n\nOne of the most common transformations is to plot data by taking its logarithm\n(to the base-10). This transformation is useful to display changes across\ndisparate scales. Using .colors.LogNorm normalizes the data via\n$log_{10}$. In the example below, there are two bumps, one much smaller\nthan the other. Using .colors.LogNorm, the shape and location of each bump\ncan clearly be seen:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport matplotlib.cbook as cbook\n\nN = 100\nX, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]\n\n# A low hump with a spike coming out of the top right. Needs to have\n# z/colour axis on a log scale so we see both hump and spike. linear\n# scale only shows the spike.\nZ1 = np.exp(-(X)**2 - (Y)**2)\nZ2 = np.exp(-(X * 10)**2 - (Y * 10)**2)\nZ = Z1 + 50 * Z2\n\nfig, ax = plt.subplots(2, 1)\n\npcm = ax.pcolor(X, Y, Z,\n norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),\n cmap='PuBu_r')\nfig.colorbar(pcm, ax=ax, extend='max')\n\npcm = ax.pcolor(X, Y, Z, cmap='PuBu_r')\nfig.colorbar(pcm, ax=ax, extend='max')\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Symmetric logarithmic\n---------------------\n\nSimilarly, it sometimes happens that there is data that is positive\nand negative, but we would still like a logarithmic scaling applied to\nboth. In this case, the negative numbers are also scaled\nlogarithmically, and mapped to smaller numbers; e.g., if vmin=-vmax,\nthen they the negative numbers are mapped from 0 to 0.5 and the\npositive from 0.5 to 1.\n\nSince the logarithm of values close to zero tends toward infinity, a\nsmall range around zero needs to be mapped linearly. The parameter\n*linthresh* allows the user to specify the size of this range\n(-*linthresh*, *linthresh*). The size of this range in the colormap is\nset by *linscale*. When *linscale* == 1.0 (the default), the space used\nfor the positive and negative halves of the linear range will be equal\nto one decade in the logarithmic range.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 100\nX, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]\nZ1 = np.exp(-X**2 - Y**2)\nZ2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots(2, 1)\n\npcm = ax.pcolormesh(X, Y, Z,\n norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,\n vmin=-1.0, vmax=1.0, base=10),\n cmap='RdBu_r')\nfig.colorbar(pcm, ax=ax, extend='both')\n\npcm = ax.pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))\nfig.colorbar(pcm, ax=ax, extend='both')\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Power-law\n---------\n\nSometimes it is useful to remap the colors onto a power-law\nrelationship (i.e. $y=x^{\\gamma}$, where $\\gamma$ is the\npower). For this we use the .colors.PowerNorm. It takes as an\nargument *gamma* (*gamma* == 1.0 will just yield the default linear\nnormalization):\n\n

#### Note

There should probably be a good reason for plotting the data using\n this type of transformation. Technical viewers are used to linear\n and logarithmic axes and data transformations. Power laws are less\n common, and viewers should explicitly be made aware that they have\n been used.

\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 100\nX, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]\nZ1 = (1 + np.sin(Y * 10.)) * X**(2.)\n\nfig, ax = plt.subplots(2, 1)\n\npcm = ax.pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=0.5),\n cmap='PuBu_r')\nfig.colorbar(pcm, ax=ax, extend='max')\n\npcm = ax.pcolormesh(X, Y, Z1, cmap='PuBu_r')\nfig.colorbar(pcm, ax=ax, extend='max')\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Discrete bounds\n---------------\n\nAnother normalization that comes with Matplotlib is .colors.BoundaryNorm.\nIn addition to *vmin* and *vmax*, this takes as arguments boundaries between\nwhich data is to be mapped. The colors are then linearly distributed between\nthese \"bounds\". For instance:\n\n.. ipython::\n\n In : import matplotlib.colors as colors\n\n In : bounds = np.array([-0.25, -0.125, 0, 0.5, 1])\n\n In : norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)\n\n In : print(norm([-0.2,-0.15,-0.02, 0.3, 0.8, 0.99]))\n [0 0 1 2 3 3]\n\nNote: Unlike the other norms, this norm returns values from 0 to *ncolors*-1.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 100\nX, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]\nZ1 = np.exp(-X**2 - Y**2)\nZ2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots(3, 1, figsize=(8, 8))\nax = ax.flatten()\n# even bounds gives a contour-like effect\nbounds = np.linspace(-1, 1, 10)\nnorm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\npcm = ax.pcolormesh(X, Y, Z,\n norm=norm,\n cmap='RdBu_r')\nfig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')\n\n# uneven bounds changes the colormapping:\nbounds = np.array([-0.25, -0.125, 0, 0.5, 1])\nnorm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\npcm = ax.pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')\nfig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')\n\npcm = ax.pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))\nfig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TwoSlopeNorm: Different mapping on either side of a center\n----------------------------------------------------------\n\nSometimes we want to have a different colormap on either side of a\nconceptual center point, and we want those two colormaps to have\ndifferent linear scales. An example is a topographic map where the land\nand ocean have a center at zero, but land typically has a greater\nelevation range than the water has depth range, and they are often\nrepresented by a different colormap.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = cbook.get_sample_data('topobathy.npz', asfileobj=False)\nwith np.load(filename) as dem:\n topo = dem['topo']\n longitude = dem['longitude']\n latitude = dem['latitude']\n\nfig, ax = plt.subplots()\n# make a colormap that has land and ocean clearly delineated and of the\n# same length (256 + 256)\ncolors_undersea = plt.cm.terrain(np.linspace(0, 0.17, 256))\ncolors_land = plt.cm.terrain(np.linspace(0.25, 1, 256))\nall_colors = np.vstack((colors_undersea, colors_land))\nterrain_map = colors.LinearSegmentedColormap.from_list('terrain_map',\n all_colors)\n\n# make the norm: Note the center is offset so that the land has more\n# dynamic range:\ndivnorm = colors.TwoSlopeNorm(vmin=-500., vcenter=0, vmax=4000)\n\npcm = ax.pcolormesh(longitude, latitude, topo, rasterized=True, norm=divnorm,\n cmap=terrain_map,)\n# Simple geographic plot, set aspect ratio beecause distance between lines of\n# longitude depends on latitude.\nax.set_aspect(1 / np.cos(np.deg2rad(49)))\nfig.colorbar(pcm, shrink=0.6)\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Custom normalization: Manually implement two linear ranges\n----------------------------------------------------------\n\nThe .TwoSlopeNorm described above makes a useful example for\ndefining your own norm.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class MidpointNormalize(colors.Normalize):\n def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):\n self.vcenter = vcenter\n colors.Normalize.__init__(self, vmin, vmax, clip)\n\n def __call__(self, value, clip=None):\n # I'm ignoring masked values and all kinds of edge cases to make a\n # simple example...\n x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]\n return np.ma.masked_array(np.interp(value, x, y))\n\n\nfig, ax = plt.subplots()\nmidnorm = MidpointNormalize(vmin=-500., vcenter=0, vmax=4000)\n\npcm = ax.pcolormesh(longitude, latitude, topo, rasterized=True, norm=midnorm,\n cmap=terrain_map)\nax.set_aspect(1 / np.cos(np.deg2rad(49)))\nfig.colorbar(pcm, shrink=0.6, extend='both')\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 0 }